There are several categorizations of noise labels. One commonly used categorization depends on whether or not the noisy label depends on the features. If the noisy label generation process is conditionally independent of the features, then a noise transition matrix \(T_{c \times c}\) , where \(c\)  is the number of classes, is sufficient to describe the label noise generation process. Each entry in \(T_{ij} = p_{ij}\)  is a probability such that the true label will be changed into a noisy label with probability \(p_{ij}\) . If the observed label is different from the true label with a uniform probability, then the noise is considered to be label-independent and this noise is called considered symmetric or uniform noise. If the observed label is changed from the true label with probabilities depending on the original ground truth, then the noise is label-dependent and called asymmetric noise. On the other hand, if the corruption process depends on the features and labels, the label noise is called instance-dependent. A more recent study proposes a new but practical assumption within instance-dependent label noise, defined as part-dependent label noise, where the noise depends partially on an instance [1]}.
